Is this a video course?

No. This is an interactive, slide-based learning platform. Each lesson has rich text, animated diagrams, live code editors, and quizzes. You learn by reading, interacting, and doing, not by watching videos passively.

How long do I have access?

Forever. Both pricing tiers are one-time payments with lifetime access. This includes all current 766 lessons and any future content we add.

What level of experience do I need?

None. We start from absolute basics like 'What is latency?' and build up to distributed consensus protocols. The Foundation level assumes zero prior knowledge of system design.

How much does the system design course cost?

7.99 US dollars for lifetime access globally, or 499 Indian rupees for lifetime access in India. One-time payment, no subscription, no hidden fees. 11 lessons are free with no signup required.

What technologies are covered?

Everything from DNS and load balancers to Kubernetes, Kafka, distributed databases, consensus protocols, stream processing, security architecture, and observability. We cover principles and real-world implementations used at Netflix, Google, Amazon, Uber, Stripe, and more.

Is this useful for system design interview preparation?

Yes. The lessons are structured around the exact topics asked in system design interviews at FAANG and top-tier companies. Interactive diagrams help you practice whiteboard-style explanations. Covers everything from URL shortener design to distributed payment systems.

How is this different from ByteByteGo or Educative?

766 interactive lessons (4x more than most competitors), 16 different diagram types that build step by step, real production examples from Netflix, Google, Amazon, Uber, and Stripe, and lifetime access for a one-time payment of $7.99 instead of annual subscriptions costing 100 to 200 dollars per year.

Why does the chunk boundary set a ceiling on answer quality that a better model or a reranker cannot lift?

Retrieval can only return text that lives inside a chunk, so a fact split across two chunks is never handed to the model in one piece, Retrieval returns whole chunks, and nothing split across two chunks arrives intact. The boundary fixes what can ever be retrieved together, upstream of the model and the reranker. Neither can reason over or reorder text that was never returned.

A chunk covers three unrelated topics. What specifically goes wrong at retrieval time?

The single vector for the chunk averages the three meanings into a blurred vector that matches no query sharply, and the extra text adds noise to the prompt, One chunk becomes one vector standing for its whole meaning. Averaging several topics produces a vector that sits between them and matches none precisely, so retrieval gets vague. The unrelated text also fills the prompt with noise that can pull the model off the answer.

How does adding overlap between chunks prevent an answer from being lost, and what does it cost?

Each chunk starts a few tokens before the previous one ended, so a sentence sitting on a seam appears whole in at least one chunk, at the cost of duplicated text and more vectors, Overlap slides the window: the next chunk repeats the tail of the previous one, so the overlap zone covers the seam and a straddling sentence lands whole in a chunk. The overlapped tokens are stored twice, so you pay duplication and extra vectors for the insurance.

A teammate wants to set every chunk to 512 tokens with 50 overlap across all content because a blog post recommended it. What is the accurate response?

It is a reasonable starting point, but the right size depends on your documents and queries and content type, so it must be tuned against your own retrieval eval, There is no universal optimum, because the right size depends on how ideas run in your documents, what users ask, and whether the content is prose, code, tables, or transcripts. Treat 512 and 50 as the first experiment, then measure size, overlap, and strategy against a real question set and keep what wins.

Chunking: The First Lever on Retrieval Quality

Name: System Design Masterclass
Availability: InStock

in Retrieval Rag

This lesson is part of the ML Platform Engineering course. Unlock the full course, a separate one-time payment, for lifetime access.

One-time payment. Lifetime access. No subscription.

Global

$20/lifetime

One-time payment via PayPal

Get Lifetime Access

5 Foundation lessons free, try before you buy

India Special

India

₹999/lifetime

One-time via UPI, cards, or net banking

Get Lifetime Access

5 Foundation lessons free, try before you buy

“The interactive diagrams alone are worth way more than the price.”

Anish K., Senior Engineer at a Bengaluru startup

Every ML Platform lesson, Foundation to Advanced

Real architecture diagrams with real tool logos

MLOps, LLMOps, and data engineering in depth

Quizzes with instant feedback

Built and reviewed by senior engineers

Lifetime access, all future content included

The first 5 Foundation lessons of this course are free, no payment needed. Try them now